🎧 EchoChamber — Room Acoustic Fingerprinting

---

EchoChamber is a web-based system that analyzes room acoustics using audio recordings and generates a unique "fingerprint" of the space. It focuses on capturing how sound behaves in real environments and storing that information in a structured, usable format.

---

🚀 What This Project Does

The application records a short audio sample from a room and processes it to extract acoustic characteristics such as:

• Reverberation time (RT60) across frequencies
• Early reflections
• Phase coherence and randomness
• Group delay metrics

These features are combined into a fingerprint that represents the acoustic identity of a room.

---

🧠 How It Works

1. Audio Capture — Frontend

The user records audio (typically a sweep signal). This recording is sent to the backend as a .wav file along with metadata like room name and capture duration.

---

2. Audio Processing — Backend

The backend:

• Extracts the raw WAV buffer
• Parses the signal
• Applies DSP techniques like inverse sweep and reflection detection

---

3. Fingerprint Generation

From the processed signal, the system computes:

• RT60 values for multiple frequency bands
• Phase-related metrics
• Early reflections

A hash is generated to uniquely identify the fingerprint.

---

4. Database Storage

The data is stored using Prisma in a structured format:

Entity	Description
Space	Represents the room
Measurement	Recording session
Fingerprint	Computed acoustic data
Reflections	Early reflection points

All related records are created inside a transaction to maintain consistency.

---

5. Authentication

The system uses cookie-based authentication with JWT:

• token (httpOnly) — actual authentication
• echochamber_session — session hint for UI

Protected routes like /analyze require a valid token.

---

🛠️ Tech Stack

Frontend

• TypeScript
• Fetch API (with credentials)
• Browser Audio APIs

Backend

• Node.js
• Express
• Prisma ORM
• JWT Authentication

Audio / DSP

• Custom signal processing utilities
• WAV parsing
• Fingerprint generation

---

⚙️ Environment Variables

Create a .env file in the backend directory:

JWT_SECRET=your_secret_key
JWT_EXPIRES_IN=1h
DATABASE_URL=your_database_url
NODE_ENV=development

---

📦 Installation

# Install dependencies
npm install

# Start backend
npm run dev

# Start frontend
npm run dev

---

🔐 Auth Flow

1. Login sets cookies: token and echochamber_session
2. Requests include cookies via:

credentials: "include"

3. Backend reads and verifies the token from cookies
4. Invalid or expired tokens are cleared automatically

---

⚠️ Notes

• Audio processing can take time depending on file size
• Avoid aborting requests during processing — this can interrupt DB transactions
• JWT expiry should be set correctly ("1h" instead of 3600)

---

📌 Future Improvements

• Move processing to background jobs
• Add retry handling for failed captures
• Visualize acoustic fingerprints
• Compare fingerprints across rooms

---

🧑‍💻 Author

Built to explore:

• Audio signal processing
• Backend architecture
• Real-world problem solving

---

📄 License

Distributed under the MIT License.